Beyond Borders: How MongoDB’s Talent Sourcing Team Fuels Global Hiring

Sunny Kathuria
August 16, 2023 | Updated: July 8, 2024

Our Talent Sourcing team is an integral part of MongoDB’s Talent Acquisition organization. Hear from Sunny Kathuria, Senior Manager of Sourcing, to learn more about his growing team and how team members have access to learning and development opportunities that are transforming their careers.

About the Sourcing Team

The Talent Sourcing team is centralized in India with global support capabilities and is divided into specialized sourcing groups by business units: Sales, Engineering & Product, Customer Engineering, and Corporate & Marketing.

The team’s strategy enables sourcers to act as true business partners to recruiters and hiring managers. Overall, we work towards the goal of generating a steady candidate flow for current and future positions by identifying, engaging, and networking with relevant talent pools of candidates, ultimately reducing the time to and cost of hire.

Along with sourcing talent, we have a group of Talent Sourcers who support open positions in India and engage with candidates over the phone, email, and LinkedIn InMail to prequalify them for recruiters.

The Talent Sourcing team at MongoDB contributes to nearly 25% of the overall hiring that takes place globally. The success of the team can be attributed to each sourcers’ knowledge of the industry, their effective utilization of sourcing channels, and their strong collaboration with recruiters and hiring managers.

Photo of the MongoDB Talent Sourcing Team

Hear from some of our team members

Deepak Kumar Tyagi, Sr. Sourcer - Engineering and Product

For over three years, MongoDB's vibrant culture and dedication to diversity and inclusion have continuously inspired me. The flexibility and learning opportunities here have greatly contributed to my professional development. Whether it's receiving mentorship from exceptional leaders, mentoring junior colleagues, or collaborating with global stakeholders, I've experienced a genuine investment in mutual success that is rare in any company. I'm thankful to be part of such a dynamic and supportive organization that prioritizes work-life balance, allowing us to tailor our schedules to meet both professional and personal needs. This flexibility has been instrumental in my success and well-being. I feel privileged to work for a company that truly values its employees, fosters both professional and personal growth, and provides a solid foundation for achieving success.

Simran Gupta - Sourcer - Customer Engineering

My time at MongoDB has been defined by continuous learning and substantial personal development. Each day presents fresh opportunities to enhance my skills and knowledge, contributing significantly to my professional growth. Engaging with stakeholders globally, participating in team-building activities, receiving comprehensive training on various tools, and collaborating extensively across teams have all been instrumental in my professional growth. MongoDB's commitment to growth is evident in its environment, fostering a culture where continuous improvement is a daily pursuit toward achieving professional excellence. The steadfast support from my team and guidance from my manager have been pivotal in my journey toward success and ongoing enhancement. I feel privileged to be part of MongoDB, a company that not only prioritizes personal and professional development but also demonstrates deep respect and investment in its employees.

Melwin Dsouza, Sourcer - Corporate & Marketing

What sets MongoDB apart is its unique work culture. The inclusive and supportive environment motivates me to bring my best to the table each day. I am grateful for the constant support provided by leadership, who have been instrumental in my growth and development. There is a consistent focus on recognising employees which further reinforces my sense of accomplishment and fuels my drive to excel. One aspect that truly stands out for me is MongoDB's remote working policy, which has been a boon for maintaining a healthy work-life balance.

AmnaUmul Khair - Sourcer, Sales

Since joining MongoDB, I've been truly impressed by its incredible culture and inclusive work environment, which strongly supports diversity and inclusion. The collaborative atmosphere here is outstanding, fostering a space where ideas thrive, teamwork is valued, and collective achievements are celebrated, regardless of where you come from. The leadership's commitment to diversity and inclusion is evident through their ongoing support, and I’m grateful to have experienced it firsthand. For instance, during Ramadan, my leaders ensured I could choose my work schedule and avoid being overburdened, fostering a strong sense of belonging. I've witnessed a sincere commitment to mutual growth and am continually amazed by the numerous learning opportunities at MongoDB, which have helped me grow both personally and professionally. Shortly after joining, I had the privilege of learning from exceptional leaders, driving my professional growth and preparing me to succeed in finding top talent for the company.

Ankur Sharma, Sourcer - Sales

Being part of the MongoDB Talent Sourcing team has been an incredibly exciting experience for me. I have the privilege of working alongside some of the brightest and most hardworking individuals with a diverse range of skills. One aspect that makes working at MongoDB truly unique is the opportunity to engage with various business units and global stakeholders, each with their own distinct cultures. This dynamic environment ensures that my learning curve is always steep and keeps me continuously challenged. I am proud to be a part of a company that fosters a top-notch culture where we not only work hard to deliver exceptional results, but we also celebrate our achievements and enjoy ourselves to the fullest.

Garima Fulara, Senior Sourcer - Corporate & Marketing

Since joining the team three years ago, each day has presented me with valuable learning opportunities and a platform for personal growth. Being a part of the Talent Sourcing team brings me immense pride, as my contributions are pivotal in driving the company’s growth by aligning the ideal professionals with the right positions. During my time at MongoDB, I’ve witnessed significant expansion in terms of employee numbers, new regions, business units, products, and more. Knowing that I’ve played a role in facilitating this growth fills me with a strong sense of accomplishment.

How to succeed on the team

We look for individuals with strong research skills, knowledge of LinkedIn Recruiter, and a drive to go above and beyond to find great candidates, especially for niche roles and geographies. If you are applying to be a tech sourcer, you need strong conceptual technical knowledge of topics such as MongoDB fundamentals, products, and competitors; databases; software development lifecycle (SDLC); web services and microservices; DevOps, DataOps, and TechOps; and distributed systems. Experience in sourcing from unconventional tools such as GitHub is highly desirable.

Our interview process involves a live sourcing test followed by interviews with the hiring manager, department head, and stakeholders. For sourcing tests, we ask candidates to read a job description, prepare a boolean based on their understanding of the job requirements, and then run a search. Candidates are assessed on their approach and engagement with the interviewer throughout the conversation, more than the number and quality of search results.

The rest of the interview rounds focus on culture, relationship-building skills, communication, and articulation. Overall, if you have attention to detail, great communication skills (sharing your observations and asking the right questions), are research-oriented and can be creative with boolean strings, we would love to network with you!

Interested in pursuing a career on the Talent Sourcing team at MongoDB? We have several open roles and would love for you to transform your career with us!

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Building AI with MongoDB: Announcing the First Qualifiers for the Innovators Program

Artificial Intelligence is igniting so many brilliant ideas for new products and services. But turning those ideas into reality is a path that even the brightest minds struggle to navigate without some help along the way. That’s why we launched the MongoDB AI Innovators Program back in June this year. Access to expert technical advice, free MongoDB Atlas credits, co-marketing opportunities, and – for eligible startups, introductions to potential venture investors – come together to help you “build the next big thing” in AI. Since opening the program, we’ve received applications from around the world addressing every industry and spanning the spectrum of generative to analytical AI use cases. From enterprise chat and video bots that improve customer service and unlock insights from vast internal information repositories, conversational intelligence for sales reps, AI agents for workflow orchestration, tools for talent recruitment and retention, identifying workplace burnout through to news classifiers and summarization, personal wellbeing assistants, and the generation of bedtime stories for children that make science and technology more accessible to them. We’ve been amazed by the breadth and pace of innovators building AI on top of MongoDB Atlas. In this blog post, I want to share an overview of three startups that have just qualified from our AI Innovators Program. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Elevating the edge experience: Deploy AI anywhere with Cloneable and MongoDB Cloneable provides the application layer that brings AI to any device at the edge of the network. The Cloneable platform empowers developers to craft dynamic applications using intuitive low/no-code tools, instantly deployable to a spectrum of devices - mobiles, IoT devices, robots, and beyond. By harnessing machine learning models, a business can seamlessly leverage complex technologies across its operations. Models are pushed down to the device where they are converted to a native embedded format such as CoreML. From here, they are executed by the device’s neural engine to provide low latency inference, computer vision, and augmented reality. Cloneable uses MongoDB Atlas Device Sync to persist data locally on the device and sync it to the Atlas database backend in the cloud. This unique synergy creates an ecosystem where enterprise apps become real-time gateways to track, measure, inspect, and respond to events across an operation. The company is also exploring creating vector embeddings from images and data collected on devices and storing them in Atlas Vector Search . With this expanded functionality, users can better search and analyze events collected from the field. Predicting risks to public safety ExTrac draws on thousands of data sources identified by domain experts, using AI-powered analytics to locate, track and forecast both digital and physical risks to public safety in real-time. Initially serving Western governments to predict risks of emerging or escalating conflicts overseas, ExTrac is expanding into enterprise use cases for reputational management, operational risk, and content moderation. “Data is at the core of what we do. Our domain experts find and curate relevant streams of data, and then we use AI to anonymize and make sense of it at scale”, said Matt King, CEO at ExTrac. “We take a base model, such as RoBERTa or an LLM, and fine-tune it with our own labeled data to create domain-specific models capable of identifying and classifying threats in real-time.” Asked about why ExTrac built on MongoDB Atlas, King said “The flexibility of the document data model allows us to land, index, and analyze data of any shape and structure – no matter how complex. This helps us unlock instant insights for our customers.” King went on to say “ Atlas Vector Search is also proving to be incredibly powerful across a range of tasks where we use the results of the search to augment our LLMs and reduce hallucinations. We can store vector embeddings right alongside the source data in a single system, enabling our developers to build new features way faster than if they had to bolt-on a standalone vector database - many of which limit the amount of data that can be returned if it has meta-data attached to it. We are also moving beyond text to vectorize images and videos from our archives dating back over a decade. Being able to query and analyze data in any modality will help us to better model trends, track evolving narratives, and predict risk for our customers.” Access to technical expertise provided by the AI Innovators Program will help ExTrac manage the ever-growing size of its data sets – keeping performance high and costs low as the business scales. Using AI to cut maritime emissions and risk CetoAI provides predictive analytics for the maritime industry; combining high-frequency data, engineering expertise, and artificial intelligence the company reduces machinery breakdowns, cuts carbon emissions, and manages operational risk. Sensors installed onto each vessel generate real-time data feeds of engine and vessel performance. The data is used by the company’s AI models for predictive maintenance, optimizing fuel consumption, and carbon intensity forecasting, with the outputs consumed by the vessel’s crew, owners, and insurers. The data feeds generated by CetoAI are highly complex. Sensors on each vessel emit around 90,000 JSON documents daily. Each document stores around 100 unique time-series measurements, all requiring heavy-duty analytics processing before feeding machine learning models. It was these demands that led CetoAI’s engineering team to select MongoDB, migrating from a standalone time-series database that couldn’t keep pace with business growth. Sensor measurements from each data feed are streamed through Microsoft Azure’s IoT hub and ingested into MongoDB Atlas’ purpose-built time-series database collections . Here MongoDB window functions process and transform the data before serving it to CetoAI’s machine-learning models built with PyTorch and Scikit-Learn. CetoAI is now exploring additional capabilities available with MongoDB to expand its offerings. Atlas Device Sync can persist data locally and sync it between vessels and the cloud, withstanding the loss of network connectivity. Atlas Vector Search can be used for Retrieval Augmented Generation with the company’s LLMs. These are being developed to help crews diagnose and remediate equipment failures using natural language queries. Access to Atlas credits and expert support provided as part of the AI Innovators program enable CetoAI to accelerate and derisk the delivery of these new services. Get started Today we’ve focused on just three startups – there are many more that are already enjoying the benefits of the AI Innovators Program . We have more places left, but they are filling up fast, so sign up directly on the program’s web page. Head over to our quick-start guide to get started with Atlas Vector Search today. Also, check out our MongoDB for Artificial Intelligence resources page for all of the latest best practices to get you started on building the “next big thing” with AI.

August 16, 2023

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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024